Then, given a dataset of \(N\)  labeled images \((X, Y)\) , we minimize the error \(\frac{1}{N}\sum _{i=1}^N{\mathcal {L}(\text{BiMoNN}(x_i), y_i)}\)  using Adam [1]}. We compute the loss gradient with the backpropagation algorithm [2]}. In practice, we replace \(f^+(b)\)  by \(f^+(b) + 0.5\)  to help with training for all BiSE and \(\text{LUI}\)  operators. The convolution weights \(W\)  follow the kaiming uniform initialization [3]}, and the biases begin at \(f^+(2) + 0.5 = 0.63\) . We initialize The BiSE and \(\text{LUI}\)  scaling factors \(p\)  at 0. That may not be optimal, as it could bias the BiSE towards some operations and Sels. Could we find other unbiased methods toward a particular operation or Sel ? This is left for further research.
